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I have already trained a 32bit weight. Can I make it be a pretrain weight and train by quantization aware(by using tf.contrib.quantize.create_training_graph(quant_delay=300000))?
Can someone guide …
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aimet version: 1.28
SNPE version: 2.14
deploy platform: SM8550 DSP w8 a8 bias32
I have a model, and the backbone of this model is MobileNetV3. As you know, MobileNetV3 primarily consists of poi…
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Hi everyone
Is it expected behavior that the quantization-aware training in QKeras is much slower than normal training in Keras? And if so, out of interest, where does the overhead come from? From …
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**System information**
- TensorFlow version (you are using): 2.4.1
- Are you willing to contribute it (Yes/No): Yes
**Describe the feature and the current behavior/state.**
When using `tf…
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Hello I am trying to apply quantization aware training for mobilenet, and I am testing on the mnist dataset, the floating point models works very well but the moment I add the quantization I have a ve…
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Hello,
I am interested in using your algorithm to train a model without utilizing Sensitivity Aware and vector quantization, but instead focusing on using Morton sorting and compression, and then s…
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Hi,
Have you tried quantizing Mamba? Do you plan on releasing quantized versions?
Can you share your thoughts on quantizing Mamba, given the sensitivity of the model's recurrent dynamics?
Thanks
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### **Initial action plans**
Copying these things from the wav2vec2 repo for safe housekeeping.
* An immediate quantize could be to convert the fine-tuned model using TFLite APIs. [Post-trainin…
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I used mtq.INT8_default_CFG as recommended for CNN networks (mtq.quantize(model, config, forward_loop). My initial model ran at 80FPS after quantization it dropped to 40FPS? I checked the model struct…
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Prior to filing: check that this should be a bug instead of a feature request. Everything supported, including the compatible versions of TensorFlow, is listed in the overview page of each technique. …